Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model

Landslide displacement prediction is an important part of monitoring and early warning systems. Effective instrumental in reducing the risk landslide disasters. This paper proposes a model based on variational mode decomposition genetic algorithm optimization Elman neural network (VMD–GA–Elman). First, using VMD, sequence decomposed into three subsequences trend term, periodic random term. Then, appropriate influencing factors are selected for each to construct input datasets; rationality selection evaluated gray correlation analysis method. The GA–Elman used forecast item, item item. Finally, total obtained by superimposing verify performance model. A case study Shuizhuyuan (China) presented validation developed results show that this good agreement with actual situation has accuracy; it can, therefore, provide basis systems deformation.